Currently, development of a pattern recognition technology and a neural network technology using a unique feature point of an object has been actively conducted. Representatively, an automated or artificial intelligent technology may be used as an example. Such artificial intelligent technology has been widely used as a character recognition technology of automatically reading a printed or hand written text, a parking and driving system technology of enabling a vehicle to be automatically parked or driven by recognizing a lane, a tracking system technology of detecting and then tracking a predetermined object, and the like.
Currently, together with growth of a market associated with digital devices, smart phones, and the like, the artificial intelligent technology has been further variously developed. When applying a character recognition and object recognition technology in the above environment, an added value of a product may be improved and sales may increase.
So far, a pattern recognition algorithm such as character recognition and object recognition and lane recognition has been developed in a form of algorithm that generally operates in a PC-based environment. When directly applying the above technology to an embedded system, real-time recognition processing may be impossible or detection information may have a low reliability due to relatively insufficient resource and performance.
In the related art, an object feature point extraction algorithm including pattern recognition may degrade feature point extraction performance based on quantized noise of a camera. To solve the above issue, an additional processing process is required. Therefore, an increase in a calculation amount and apparatus complexity makes it difficult to install the object feature point extraction algorithm in hardware that is not based on a PC.
In hardware configuration, the object feature point extraction algorithm uses many resources and thus, may be difficult to be commercialized. Even though the object feature point extraction algorithm is commercialized, an increase in a processing time according to an increase in a calculation amount may make real-time processing difficult. Accordingly, there is a need for development of a real-time processing engine that is robust against noise and thus, shows highly reliable object feature point extraction performance and may be directly configured in hardware at a high processing rate, instead of a simple feature point extraction algorithm.